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Unverified Commit 8b49f207 authored by Max Podkorytov's avatar Max Podkorytov Committed by GitHub
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Merge branch 'develop' into fa-h512

parents 0d59f474 a6b761c3
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Showing with 602 additions and 142 deletions
profiler/src/CMakeLists.txt
View file @ 8b49f207
......@@ -58,6 +58,7 @@ if(SUPPORTED_GPU_TARGETS MATCHES "gfx9")
list(APPEND PROFILER_SOURCES profile_gemm_bias_add_reduce.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_splitk.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_universal.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_b_scale.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_universal_batched.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_universal_reduce.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_universal_streamk.cpp)
......@@ -141,6 +142,7 @@ if(SUPPORTED_GPU_TARGETS MATCHES "gfx9")
endif()
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_splitk_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_universal_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_b_scale_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_universal_batched_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_universal_reduce_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_universal_streamk_instance)
......@@ -177,5 +179,4 @@ if(DL_KERNELS)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_weight_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_weight_instance)
endif()
rocm_install(TARGETS ${PROFILER_EXECUTABLE} COMPONENT profiler)
profiler/src/profile_gemm_b_scale.cpp 0 → 100644
View file @ 8b49f207
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <initializer_list>
#include <iostream>
#include <numeric>
#include "profiler/profile_gemm_b_scale_impl.hpp"
#include "profiler_operation_registry.hpp"
enum struct GemmMatrixLayout
{
MK_KN_MN, // 0
MK_NK_MN, // 1
KM_KN_MN, // 2
KM_NK_MN, // 3
};
enum struct GemmDataType
{
F32_F32_F32, // 0
F16_F16_F16, // 1
BF16_BF16_BF16, // 2
INT8_INT8_INT8, // 3
F8_F16_F16, // 4
F16_F8_F16, // 5
F16_F16_F16_F8, // 6
F8_F8_BF16, // 7
F16_I4_F16, // 8
};
enum struct BScaleBlockTile
{
K_64, // 0
K_128, // 1
};
#define OP_NAME "gemm_b_scale"
#define OP_DESC "Int4-dequant GEMM"
int profile_gemm_b_scale(int argc, char* argv[])
{
if(argc != 16 && argc != 19)
{
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: f8@f16; 5: f16@f8; 6: "
"f16->f8; 7: f8->bf16, "
"comp f8; 8: f16@i4)\n");
printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
printf(" 1: A[m, k] * B[n, k] = C[m, n];\n");
printf(" 2: A[k, m] * B[k, n] = C[m, n];\n");
printf(" 3: A[k, m] * B[n, k] = C[m, n])\n");
printf("arg4: B scale block tile (0: 64, 1: 128):\n");
printf("arg5: verification (0: no; 1: yes)\n");
printf("arg6: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg7: print tensor value (0: no; 1: yes)\n");
printf("arg8: time kernel (0=no, 1=yes)\n");
printf("arg9 to 14: M, N, K, StrideA, StrideB, StrideC\n");
printf("arg15: split k into mulitiple batch\n");
printf("optional:\n");
printf("arg16: number of warm-up cycles (default 1)\n");
printf("arg17: number of iterations (default 10)\n");
printf("arg18: memory for rotating buffer (default 0, size in MB)\n");
exit(1);
}
printf("Start profiling\n");
const auto data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
const auto layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
const auto B_scale_block = static_cast<BScaleBlockTile>(std::stoi(argv[4]));
const bool do_verification = std::stoi(argv[5]);
const int init_method = std::stoi(argv[6]);
const bool do_log = std::stoi(argv[7]);
const bool time_kernel = std::stoi(argv[8]);
const int M = std::stoi(argv[9]);
const int N = std::stoi(argv[10]);
const int K = std::stoi(argv[11]);
const int StrideA = std::stoi(argv[12]);
const int StrideB = std::stoi(argv[13]);
const int StrideC = std::stoi(argv[14]);
const int KBatch = std::stoi(argv[15]);
printf("M:%d, N:%d, K:%d, StrideA:%d, StrideB:%d, StrideC:%d, KBatch:%d\n",
M,
N,
K,
StrideA,
StrideB,
StrideC,
KBatch);
int n_warmup = 1;
int n_iter = 10;
uint64_t rotating = 0;
if(argc == 19)
{
n_warmup = std::stoi(argv[16]);
n_iter = std::stoi(argv[17]);
rotating = std::stoull(argv[18]) * 1024 * 1024;
printf("n_warmup:%d, n_iter:%d, rotating:%lu\n", n_warmup, n_iter, rotating);
}
using F32 = float;
using F16 = ck::half_t;
using I4 = ck::pk_i4_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
auto profile = [&](auto a_type,
auto b_type,
auto b_scale_type,
auto comp_type,
auto acc_type,
auto c_type,
auto scale_block_k,
auto a_layout,
auto b_layout,
auto c_layout) {
using ADataType = decltype(a_type);
using BDataType = decltype(b_type);
using BScaleDataType = decltype(b_scale_type);
using ComputeDataType = decltype(comp_type);
using AccDataType = decltype(acc_type);
using CDataType = decltype(c_type);
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout);
using CLayout = decltype(c_layout);
const int DefaultStrideA = ck::is_same_v<ALayout, Row> ? K : M;
const int DefaultStrideB = ck::is_same_v<BLayout, Row> ? N : K;
const int DefaultStrideC = ck::is_same_v<CLayout, Row> ? N : M;
bool pass = ck::profiler::profile_gemm_b_scale_impl<ADataType,
BDataType,
BScaleDataType,
ComputeDataType,
AccDataType,
CDataType,
scale_block_k,
ALayout,
BLayout,
CLayout>(
do_verification,
init_method,
do_log,
time_kernel,
M,
N,
K,
(StrideA < 0) ? DefaultStrideA : StrideA,
(StrideB < 0) ? DefaultStrideB : StrideB,
(StrideC < 0) ? DefaultStrideC : StrideC,
KBatch,
n_warmup,
n_iter,
rotating);
return pass ? 0 : 1;
};
if(data_type == GemmDataType::F16_I4_F16 && layout == GemmMatrixLayout::MK_NK_MN &&
B_scale_block == BScaleBlockTile::K_128)
{
printf("F16_I4_F16 MK_NK_MN K_128\n");
return profile(
F16{}, I4{}, F16{}, F16{}, F32{}, F16{}, ck::Number<128>{}, Row{}, Col{}, Row{});
}
else
{
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
}
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_b_scale);
profiler/src/profile_gemm_universal.cpp
View file @ 8b49f207
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <initializer_list>
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "profiler/profile_gemm_universal_impl.hpp"
#include "profiler_operation_registry.hpp"
......@@ -27,6 +27,8 @@ enum struct GemmDataType
F16_F8_F16, // 5
F16_F16_F16_F8, // 6
F8_F8_BF16, // 7
F16_I4_F16, // 8
BF16_I4_BF16, // 9
};
#define OP_NAME "gemm_universal"
......@@ -39,7 +41,7 @@ int profile_gemm_universal(int argc, char* argv[])
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: f8@f16; 5: f16@f8; 6: "
"f16->f8; 7: f8->bf16, "
"comp f8)\n");
"comp f8; 8: f16@i4; 9: bf16@i4\n");
printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
printf(" 1: A[m, k] * B[n, k] = C[m, n];\n");
printf(" 2: A[k, m] * B[k, n] = C[m, n];\n");
......@@ -103,6 +105,7 @@ int profile_gemm_universal(int argc, char* argv[])
using BF16 = ck::bhalf_t;
#if defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH) || defined(CK_USE_GFX94)
using F8 = ck::f8_t;
using I4 = ck::pk_i4_t;
#endif
using Row = ck::tensor_layout::gemm::RowMajor;
......@@ -207,6 +210,14 @@ int profile_gemm_universal(int argc, char* argv[])
{
return profile(F8{}, F8{}, F8{}, F32{}, BF16{}, Row{}, Col{}, Row{});
}
else if(data_type == GemmDataType::F16_I4_F16 && layout == GemmMatrixLayout::MK_NK_MN)
{
return profile(F16{}, I4{}, F16{}, F32{}, F16{}, Row{}, Col{}, Row{});
}
else if(data_type == GemmDataType::BF16_I4_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
{
return profile(BF16{}, I4{}, BF16{}, F32{}, BF16{}, Row{}, Col{}, Row{});
}
#endif
else
{
......
profiler/src/profile_gemm_universal_batched.cpp
View file @ 8b49f207
......@@ -31,7 +31,7 @@ enum struct GemmDataType
int profile_batched_gemm_universal(int argc, char* argv[])
{
if(argc != 18 && argc != 21)
if(argc != 19 && argc != 22)
{
// clang-format off
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
......@@ -44,11 +44,11 @@ int profile_batched_gemm_universal(int argc, char* argv[])
printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=n0, 1=yes)\n");
printf("arg8 to 17: M, N, K, StrideA, StrideB, StrideC, BatchStrideA, BatchStrideB, BatchStrideC, BatchCount\n");
printf("arg8 to 18: M, N, K, StrideA, StrideB, StrideC, BatchStrideA, BatchStrideB, BatchStrideC, BatchCount, KBatch\n");
printf("optional:\n");
printf("arg18: number of warm-up cycles (default 1)\n");
printf("arg19: number of iterations (default 10)\n");
printf("arg20: memory for rotating buffer (default 0, size in MB)\n");
printf("arg19: number of warm-up cycles (default 1)\n");
printf("arg20: number of iterations (default 10)\n");
printf("arg21: memory for rotating buffer (default 0, size in MB)\n");
// clang-format on
exit(1);
}
......@@ -56,11 +56,11 @@ int profile_batched_gemm_universal(int argc, char* argv[])
int n_warmup = 1;
int n_iter = 10;
uint64_t rotating = 0;
if(argc == 21)
if(argc == 22)
{
n_warmup = std::stoi(argv[18]);
n_iter = std::stoi(argv[19]);
rotating = std::stoull(argv[20]) * 1024 * 1024;
n_warmup = std::stoi(argv[19]);
n_iter = std::stoi(argv[20]);
rotating = std::stoull(argv[21]) * 1024 * 1024;
}
const auto data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
......@@ -83,6 +83,7 @@ int profile_batched_gemm_universal(int argc, char* argv[])
const int BatchStrideC = std::stoi(argv[16]);
const int BatchCount = std::stoi(argv[17]);
const int KBatch = std::stoi(argv[18]);
#if defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH) || defined(CK_USE_GFX94)
using F8 = ck::f8_t;
......@@ -159,6 +160,7 @@ int profile_batched_gemm_universal(int argc, char* argv[])
StrideB_,
StrideC_,
BatchCount,
KBatch,
n_warmup,
n_iter,
rotating);
......
profiler/src/profile_gemm_universal_streamk.cpp 100755 → 100644
View file @ 8b49f207
......@@ -83,8 +83,9 @@ int profile_gemm_universal_streamk(int argc, char* argv[])
rotating = std::stoull(argv[18]) * 1024 * 1024;
}
using F32 = float;
using F16 = ck::half_t;
using F32 = float;
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
#if defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH) || defined(CK_USE_GFX94)
using F8 = ck::f8_t;
......@@ -165,6 +166,22 @@ int profile_gemm_universal_streamk(int argc, char* argv[])
return profile(F8{}, F16{}, F32{}, F16{}, Row{}, Col{}, Row{});
}
#endif
else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_KN_MN)
{
return profile(BF16{}, BF16{}, F32{}, BF16{}, Row{}, Row{}, Row{});
}
else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
{
return profile(BF16{}, BF16{}, F32{}, BF16{}, Row{}, Col{}, Row{});
}
else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::KM_KN_MN)
{
return profile(BF16{}, BF16{}, F32{}, BF16{}, Col{}, Row{}, Row{});
}
else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::KM_NK_MN)
{
return profile(BF16{}, BF16{}, F32{}, BF16{}, Col{}, Col{}, Row{});
}
else
{
std::cout << "this data_type & layout is not implemented" << std::endl;
......
script/cmake-ck-dev.sh
View file @ 8b49f207
......@@ -15,9 +15,9 @@ else
fi
cmake \
-D CMAKE_PREFIX_PATH=/opt/rocm \
-D CMAKE_PREFIX_PATH=/opt/rocm/ \
-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
-D CMAKE_CXX_FLAGS="-Xclang -mllvm -Xclang -enable-post-misched=0 -std=c++17 -O3 -ftemplate-backtrace-limit=0 -fPIE -Wno-gnu-line-marker" \
-D CMAKE_CXX_FLAGS="-Xclang -mllvm -Xclang -enable-post-misched=0 -std=c++17 -O3 -ftemplate-backtrace-limit=0 -fPIE -Wno-gnu-line-marker" \
-D CMAKE_BUILD_TYPE=Release \
-D BUILD_DEV=ON \
-D GPU_TARGETS=$GPU_TARGETS \
......
script/process_perf_data.py
View file @ 8b49f207
......@@ -82,7 +82,7 @@ def parse_logfile(logfile):
StrideA=[]
StrideB=[]
StrideC=[]
if 'perf_gemm.log' in logfile:
if 'perf_gemm' in logfile and 'gemm_bilinear' not in logfile:
for line in open(logfile):
if 'Best Perf' in line:
lst=line.split()
......@@ -260,7 +260,7 @@ def main():
conn = sqlEngine.connect()
#save gemm performance tests:
if 'perf_gemm.log' in filename:
if 'perf_gemm' in filename and 'gemm_bilinear' not in filename:
#write the ck_gemm_test_params table only needed once the test set changes
#post_test_params(test_list,conn)
for i in range(1,len(results)+1):
......@@ -332,7 +332,7 @@ def main():
table_name="ck_fmha_bwd_tflops"
tflops_base = get_baseline(table_name,conn)
store_new_test_result(table_name, results, testlist, branch_name, node_id, gpu_arch, compute_units, rocm_vers, hip_vers, environment, conn)
store_new_test_result(table_name, results, testlist, branch_name, node_id, gpu_arch, compute_units, rocm_vers, hip_vers, environment, sqlEngine)
conn.close()
#compare the results to the baseline if baseline exists
......
script/process_perf_data.sh
View file @ 8b49f207
......@@ -11,9 +11,22 @@
#process results
python3 process_perf_data.py perf_gemm.log
python3 process_perf_data.py perf_onnx_gemm.log
python3 process_perf_data.py perf_resnet50_N256.log
python3 process_perf_data.py perf_resnet50_N4.log
file=./perf_onnx_gemm_gfx10.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_onnx_gemm_gfx10.log
fi
file=./perf_onnx_gemm_gfx11.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_onnx_gemm_gfx11.log
fi
file=./perf_onnx_gemm_gfx12.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_onnx_gemm_gfx12.log
fi
file=./perf_fmha_fwd_gfx942.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_fmha_fwd_gfx942.log
......
script/process_qa_data.sh
View file @ 8b49f207
......@@ -24,6 +24,18 @@ python3 process_perf_data.py perf_splitK_gemm.log
python3 process_perf_data.py perf_onnx_gemm.log
python3 process_perf_data.py perf_mixed_gemm.log
file=./perf_onnx_gemm_gfx10.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_onnx_gemm_gfx10.log
fi
file=./perf_onnx_gemm_gfx11.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_onnx_gemm_gfx11.log
fi
file=./perf_onnx_gemm_gfx12.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_onnx_gemm_gfx12.log
fi
file=./perf_fmha_fwd_gfx942.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_fmha_fwd_gfx942.log
......
script/run_full_performance_tests.sh
View file @ 8b49f207
......@@ -5,7 +5,7 @@
# post your new test results to the database and compare them to the baseline
# please contact Illia.Silin@amd.com for more details
#
# run the script as "./run_full_performance_tests.sh <verification> <tag for your test environment> <branch name> < node name>
# run the script as "./run_full_performance_tests.sh <verification> <tag for your test environment> <branch name> <node name>
# input arguments:
# verification = 0 : do not verify result correctness on CPU
# = 1 : verifuy correctness on CPU (may take a long time)
......
script/run_gemm_performance_tests.sh 0 → 100755
View file @ 8b49f207
#!/bin/bash
#
# in order to run this script you'd first need to build the ckProfiler executable in ../build/bin/
# run the script as "./run_gemm_performance_tests.sh <verification> <tag for your test environment> <branch name> <node name> <arch>
# input arguments:
# verification = 0 : do not verify result correctness on CPU
# = 1 : verify correctness on CPU (may take a long time)
# environment tag : a string describing the specifics of your test environment
# branch name : name of the branch in git repo (git status | grep -e 'On branch')
# node name : $hostname
# arch : GPU architecture, e.g. "gfx9" or "gfx1100"
#get the command line arguments:
export verify=$1
echo 'Verification: ' $verify
export env_type=$2
echo 'Environment type: ' $env_type
export branch=$3
echo 'Branch name: ' $branch
export host_name=$4
echo 'Host name: ' $host_name
export arch=$5
echo 'GPU architecture: ' $arch
function print_log_header(){
rm -f $1;
echo 'On branch ' $3 &> $1;
echo 'Node name: ' $4 >> $1;
#get GPU_arch and number of compute units from rocminfo
echo -n "GPU_arch: " >> $1; rocminfo | grep "Name:" | grep "gfx" >> $1;
rocminfo | grep "Compute Unit:" >> $1;
hipcc --version | grep -e 'HIP version' >> $1;
echo 'Environment type: ' $2 >> $1;
/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> $1;
}
#run ONNX gemm tests
export onnx_log="perf_onnx_gemm_$arch.log"
print_log_header $onnx_log $env_type $branch $host_name
./profile_onnx_gemm.sh gemm 0 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
./profile_onnx_gemm.sh gemm 1 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
script/run_performance_tests.sh
View file @ 8b49f207
#!/bin/bash
#
# in order to run this script you'd first need to build the ckProfiler executable in ../build/bin/
# run the script as "./run_performance_tests.sh <verification> <tag for your test environment> <branch name> < node name>
# run the script as "./run_performance_tests.sh <verification> <tag for your test environment> <branch name> <node name>
# input arguments:
# verification = 0 : do not verify result correctness on CPU
# = 1 : verify correctness on CPU (may take a long time)
......@@ -51,20 +51,11 @@ print_log_header $gemm_log $env_type $branch $host_name
./profile_gemm.sh gemm 2 3 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 3 3 $verify 1 0 1 | tee -a $gemm_log
#run grouped_fwd fp16 tests
export grouped_conv_fwd_log="perf_grouped_conv_fwd_fp16.log"
print_log_header $conv_fwd_log $env_type $branch $host_name
./profile_grouped_conv_fwd.sh grouped_conv_fwd 1 1 0 $verify 1 0 1 256 2>&1 | tee -a $grouped_conv_fwd_log
#run grouped_bwd_data fp16 tests
export grouped_conv_bwd_data_log="perf_grouped_conv_bwd_data_fp16.log"
print_log_header $grouped_conv_bwd_data_log $env_type $branch $host_name
./profile_grouped_conv_bwd_data.sh grouped_conv_bwd_data 1 1 $verify 1 0 1 256 2>&1 | tee -a $grouped_conv_bwd_data_log
#run grouped_bwd_weight fp16 tests
export grouped_conv_bwd_weight_log="perf_grouped_conv_bwd_weight_fp16.log"
print_log_header $grouped_conv_bwd_weight_log $env_type $branch $host_name
./profile_grouped_conv_bwd_weight.sh grouped_conv_bwd_weight 1 1 $verify 1 0 1 256 1 2>&1 | tee -a $grouped_conv_bwd_weight_log
#run ONNX gemm tests
export onnx_log="perf_onnx_gemm.log"
print_log_header $onnx_log $env_type $branch $host_name
./profile_onnx_gemm.sh gemm 0 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
./profile_onnx_gemm.sh gemm 1 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
#run resnet50 tests
export resnet256_log="perf_resnet50_N256.log"
......
test/ck_tile/batched_gemm/test_batched_gemm_util.hpp
View file @ 8b49f207
......@@ -24,12 +24,9 @@ class TestCkTileBatchedGemm : public ::testing::Test
using AccDataType = std::tuple_element_t<5, Tuple>;
using CDataType = std::tuple_element_t<6, Tuple>;
struct batched_gemm_kargs : public ck_tile::BatchedGemmHostArgs
{
};
template <typename ALayout, typename BLayout, typename CLayout>
void invoke_batched_gemm(const batched_gemm_kargs& args, const ck_tile::stream_config& s)
void invoke_batched_gemm(const ck_tile::BatchedGemmHostArgs& args,
const ck_tile::stream_config& s)
{
// The kPadM, kPadN, kPadK & kBlockPerCu should also come from the Codegen part.
constexpr bool kPadM = false;
......@@ -94,9 +91,9 @@ class TestCkTileBatchedGemm : public ::testing::Test
using Kernel =
ck_tile::BatchedGemmKernel<TilePartitioner, CodegenGemmPipeline, GemmEpilogue>;
auto kargs = Kernel::MakeKargs(args);
auto kargs = Kernel::MakeKernelArgs(args);
const dim3 grids = Kernel::GridSize(args);
const dim3 grids = Kernel::GridSize(args.M, args.N, args.k_batch, args.batch_count);
constexpr dim3 blocks = Kernel::BlockSize();
if(s.log_level_ > 0)
......@@ -185,21 +182,23 @@ class TestCkTileBatchedGemm : public ::testing::Test
c_m_n_dev_buf.SetZero();
c_m_n_dev_result.SetZero();
batched_gemm_kargs kargs{a_m_k_dev_buf.GetDeviceBuffer(),
b_k_n_dev_buf.GetDeviceBuffer(),
c_m_n_dev_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
StrideC,
BatchStrideA,
BatchStrideB,
BatchStrideC,
BatchCount};
invoke_batched_gemm<ALayout, BLayout, CLayout>(kargs,
ck_tile::BatchedGemmHostArgs args;
args.a_ptr = a_m_k_dev_buf.GetDeviceBuffer();
args.b_ptr = b_k_n_dev_buf.GetDeviceBuffer();
args.c_ptr = c_m_n_dev_buf.GetDeviceBuffer();
args.k_batch = 1;
args.M = M;
args.N = N;
args.K = K;
args.stride_A = StrideA;
args.stride_B = StrideB;
args.stride_C = StrideC;
args.batch_stride_A = BatchStrideA;
args.batch_stride_B = BatchStrideB;
args.batch_stride_C = BatchStrideC;
args.batch_count = BatchCount;
invoke_batched_gemm<ALayout, BLayout, CLayout>(args,
ck_tile::stream_config{nullptr, false});
std::cout << "Run kernel with M =" << M << " N =" << N << " K =" << K
......
test/ck_tile/gemm/CMakeLists.txt
View file @ 8b49f207
# Currently ck_tile is only built on gfx9
if(GPU_TARGETS MATCHES "gfx9")
add_gtest_executable(test_ck_tile_gemm_mem_pipeline test_gemm_mem_pipeline.cpp)
add_gtest_executable(test_ck_tile_gemm_pipeline test_gemm_pipeline.cpp)
endif()
test/ck_tile/gemm/test_gemm_mem_pipeline.cpp → test/ck_tile/gemm/test_gemm_pipeline.cpp
View file @ 8b49f207
......@@ -6,7 +6,7 @@
#include "gtest/gtest.h"
#include "ck_tile/host.hpp"
#include "test_gemm_mem_pipeline_util.hpp"
#include "test_gemm_pipeline_util.hpp"
using F16 = ck_tile::half_t;
using F32 = float;
......@@ -16,21 +16,27 @@ using Intrawave = ck_tile::integral_constant<ck_tile::GemmPipelineScheduler,
ck_tile::GemmPipelineScheduler::Intrawave>;
using Interwave = ck_tile::integral_constant<ck_tile::GemmPipelineScheduler,
ck_tile::GemmPipelineScheduler::Interwave>;
using Mem = ck_tile::integral_constant<GemmPipelineType, GemmPipelineType::Mem>;
using Comp = ck_tile::integral_constant<GemmPipelineType, GemmPipelineType::Comp>;
// clang-format off
using KernelTypes = ::testing::Types<
// ALayout, BLayout, CLayout, ADataType, BDataType, AccDataType, CDataType, GemmPipelineScheduler
std::tuple< Row, Row, Row, F16, F16, F32, F16, Intrawave>,
std::tuple< Row, Row, Row, F16, F16, F32, F16, Interwave>,
std::tuple< Row, Col, Row, F16, F16, F32, F16, Intrawave>,
std::tuple< Row, Col, Row, F16, F16, F32, F16, Interwave>,
std::tuple< Col, Row, Row, F16, F16, F32, F16, Intrawave>,
std::tuple< Col, Row, Row, F16, F16, F32, F16, Interwave>,
std::tuple< Col, Col, Row, F16, F16, F32, F16, Intrawave>,
std::tuple< Col, Col, Row, F16, F16, F32, F16, Interwave>
// ALayout, BLayout, CLayout, ADataType, BDataType, AccDataType, CDataType, GemmPipelineScheduler, PipelineType
std::tuple< Row, Row, Row, F16, F16, F32, F16, Intrawave, Mem>,
std::tuple< Row, Row, Row, F16, F16, F32, F16, Intrawave, Comp>,
std::tuple< Row, Row, Row, F16, F16, F32, F16, Interwave, Mem>,
std::tuple< Row, Col, Row, F16, F16, F32, F16, Intrawave, Mem>,
std::tuple< Row, Col, Row, F16, F16, F32, F16, Intrawave, Comp>,
std::tuple< Row, Col, Row, F16, F16, F32, F16, Interwave, Mem>,
std::tuple< Col, Row, Row, F16, F16, F32, F16, Intrawave, Mem>,
std::tuple< Col, Row, Row, F16, F16, F32, F16, Intrawave, Comp>,
std::tuple< Col, Row, Row, F16, F16, F32, F16, Interwave, Mem>,
std::tuple< Col, Col, Row, F16, F16, F32, F16, Intrawave, Mem>,
std::tuple< Col, Col, Row, F16, F16, F32, F16, Intrawave, Comp>,
std::tuple< Col, Col, Row, F16, F16, F32, F16, Interwave, Mem>
>;
// clang-format on
TYPED_TEST_SUITE(TestCkTileGemmMemPipeline, KernelTypes);
TYPED_TEST_SUITE(TestCkTileGemmPipeline, KernelTypes);
#include "test_gemm_mem_pipeline_ut_cases.inc"
#include "test_gemm_pipeline_ut_cases.inc"
test/ck_tile/gemm/test_gemm_mem_pipeline_ut_cases.inc → test/ck_tile/gemm/test_gemm_pipeline_ut_cases.inc
View file @ 8b49f207
......@@ -3,7 +3,7 @@
#pragma once
TYPED_TEST(TestCkTileGemmMemPipeline, SmallM)
TYPED_TEST(TestCkTileGemmPipeline, SmallM)
{
std::vector<int> Ms{1, 2, 3, 4, 5, 6};
constexpr int N = 1024;
......@@ -13,7 +13,7 @@ TYPED_TEST(TestCkTileGemmMemPipeline, SmallM)
this->Run(M, N, K);
}
TYPED_TEST(TestCkTileGemmMemPipeline, MidLargeM)
TYPED_TEST(TestCkTileGemmPipeline, MidLargeM)
{
std::vector<int> Ms{127, 255, 312, 799, 1573};
constexpr int N = 1024;
......@@ -23,7 +23,7 @@ TYPED_TEST(TestCkTileGemmMemPipeline, MidLargeM)
this->Run(M, N, K);
}
TYPED_TEST(TestCkTileGemmMemPipeline, PaddK)
TYPED_TEST(TestCkTileGemmPipeline, PaddK)
{
std::vector<int> Ms{127};
constexpr int N = 1024;
......@@ -33,7 +33,7 @@ TYPED_TEST(TestCkTileGemmMemPipeline, PaddK)
this->Run(M, N, K);
}
TYPED_TEST(TestCkTileGemmMemPipeline, Regular)
TYPED_TEST(TestCkTileGemmPipeline, Regular)
{
std::vector<int> Ms{512};
constexpr int N = 1024;
......@@ -43,7 +43,7 @@ TYPED_TEST(TestCkTileGemmMemPipeline, Regular)
this->Run(M, N, K);
}
TYPED_TEST(TestCkTileGemmMemPipeline, NotSupportedArgument)
TYPED_TEST(TestCkTileGemmPipeline, NotSupportedArgument)
{
constexpr int M = 512;
constexpr int N = 1025;
......
test/ck_tile/gemm/test_gemm_mem_pipeline_util.hpp → test/ck_tile/gemm/test_gemm_pipeline_util.hpp
View file @ 8b49f207
......@@ -11,36 +11,28 @@
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"
enum struct GemmPipelineType
{
Mem,
Comp
};
template <typename Tuple>
class TestCkTileGemmMemPipeline : public ::testing::Test
class TestCkTileGemmPipeline : public ::testing::Test
{
protected:
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using CLayout = std::tuple_element_t<2, Tuple>;
using ADataType = std::tuple_element_t<3, Tuple>;
using BDataType = std::tuple_element_t<4, Tuple>;
using AccDataType = std::tuple_element_t<5, Tuple>;
using CDataType = std::tuple_element_t<6, Tuple>;
static constexpr auto Scheduler = std::tuple_element_t<7, Tuple>::value;
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using CLayout = std::tuple_element_t<2, Tuple>;
using ADataType = std::tuple_element_t<3, Tuple>;
using BDataType = std::tuple_element_t<4, Tuple>;
using AccDataType = std::tuple_element_t<5, Tuple>;
using CDataType = std::tuple_element_t<6, Tuple>;
static constexpr auto Scheduler = std::tuple_element_t<7, Tuple>::value;
static constexpr auto PipelineType = std::tuple_element_t<8, Tuple>::value;
// TODO: expose tile size through test t-param ?
struct gemm_args
{
const void* p_a;
const void* p_b;
void* p_c;
ck_tile::index_t kbatch;
ck_tile::index_t M;
ck_tile::index_t N;
ck_tile::index_t K;
ck_tile::index_t stride_A;
ck_tile::index_t stride_B;
ck_tile::index_t stride_C;
};
template <bool PadM, bool PadN, bool PadK>
void invoke_gemm(const gemm_args& args, const ck_tile::stream_config& s)
void invoke_gemm(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config& s)
{
// TODO: This should be parameterized in tests
constexpr ck_tile::index_t M_Tile = 128;
......@@ -74,10 +66,17 @@ class TestCkTileGemmMemPipeline : public ::testing::Test
using Traits = ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
using BaseGemmPipeline = ck_tile::BaseGemmPipelineAgBgCrMem<
ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>>;
const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(args.K);
using BaseGemmPipeline = std::conditional_t<
PipelineType == GemmPipelineType::Mem,
ck_tile::BaseGemmPipelineAgBgCrMem<
ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>>,
ck_tile::BaseGemmPipelineAgBgCrCompV3<
ck_tile::
GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>>>;
const ck_tile::index_t k_grain = args.k_batch * K_Tile;
const ck_tile::index_t K_split = (args.K + k_grain - 1) / k_grain * K_Tile;
const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(K_split);
const bool has_hot_loop = BaseGemmPipeline::BlockHasHotloop(num_loop);
const ck_tile::TailNumber tail_num = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);
......@@ -85,27 +84,30 @@ class TestCkTileGemmMemPipeline : public ::testing::Test
constexpr bool has_hot_loop_v = has_hot_loop_.value;
constexpr auto tail_number_v = tail_number_.value;
using GemmPipeline = ck_tile::GemmPipelineAgBgCrMem<
ck_tile::UniversalGemmPipelineProblem<ADataType,
BDataType,
AccDataType,
GemmShape,
Traits,
Scheduler,
has_hot_loop_v,
tail_number_v>>;
using GemmPipeline =
std::conditional_t<PipelineType == GemmPipelineType::Mem,
ck_tile::GemmPipelineAgBgCrMem<
ck_tile::UniversalGemmPipelineProblem<ADataType,
BDataType,
AccDataType,
GemmShape,
Traits,
Scheduler,
has_hot_loop_v,
tail_number_v>>,
ck_tile::GemmPipelineAgBgCrCompV3<
ck_tile::UniversalGemmPipelineProblem<ADataType,
BDataType,
AccDataType,
GemmShape,
Traits,
Scheduler,
has_hot_loop_v,
tail_number_v>>>;
using Kernel = ck_tile::GemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
auto kargs = Kernel::MakeKargs(args.p_a,
args.p_b,
args.p_c,
args.M,
args.N,
args.K,
args.stride_A,
args.stride_B,
args.stride_C);
const dim3 grids = Kernel::GridSize(args.M, args.N, args.kbatch);
auto kargs = Kernel::MakeKernelArgs(args);
const dim3 grids = Kernel::GridSize(args.M, args.N, args.k_batch);
constexpr dim3 blocks = Kernel::BlockSize();
if(!Kernel::IsSupportedArgument(kargs))
......@@ -297,11 +299,11 @@ class TestCkTileGemmMemPipeline : public ::testing::Test
c_m_n_dev_buf.SetZero();
c_m_n_dev_result.SetZero();
gemm_args args;
args.p_a = a_m_k_dev_buf.GetDeviceBuffer();
args.p_b = b_k_n_dev_buf.GetDeviceBuffer();
args.p_c = c_m_n_dev_buf.GetDeviceBuffer();
args.kbatch = kbatch;
ck_tile::GemmHostArgs args;
args.a_ptr = a_m_k_dev_buf.GetDeviceBuffer();
args.b_ptr = b_k_n_dev_buf.GetDeviceBuffer();
args.c_ptr = c_m_n_dev_buf.GetDeviceBuffer();
args.k_batch = kbatch;
args.M = M;
args.N = N;
args.K = K;
......
test/data_type/test_custom_type.cpp
View file @ 8b49f207
......@@ -51,8 +51,11 @@ TEST(Custom_bool, TestAsType)
ck::static_for<0, size, 1>{}([&](auto i) {
right_vec.template AsType<custom_bool_t>()(Number<i>{}) = custom_bool_t{test_vec.at(i)};
});
// copy the vector
vector_type<custom_bool_t, size> left_vec{right_vec};
vector_type<custom_bool_t, size> left_vec;
// check copy assignment op
left_vec = right_vec;
// overwrite right_vec with 0s
right_vec = vector_type<custom_bool_t, size>{};
// check if values were copied correctly
ck::static_for<0, size, 1>{}([&](auto i) {
ASSERT_EQ(left_vec.template AsType<custom_bool_t>()(Number<i>{}).data, test_vec.at(i));
......@@ -129,8 +132,11 @@ TEST(Custom_int8, TestAsType)
ck::static_for<0, size, 1>{}([&](auto i) {
right_vec.template AsType<custom_int8_t>()(Number<i>{}) = custom_int8_t{test_vec.at(i)};
});
// copy the vector
vector_type<custom_int8_t, size> left_vec{right_vec};
vector_type<custom_int8_t, size> left_vec;
// check copy assignment op
left_vec = right_vec;
// overwrite right_vec with 0s
right_vec = vector_type<custom_int8_t, size>{};
// check if values were copied correctly
ck::static_for<0, size, 1>{}([&](auto i) {
ASSERT_EQ(left_vec.template AsType<custom_int8_t>()(Number<i>{}).data, test_vec.at(i));
......@@ -207,8 +213,11 @@ TEST(Custom_uint8, TestAsType)
ck::static_for<0, size, 1>{}([&](auto i) {
right_vec.template AsType<custom_uint8_t>()(Number<i>{}) = custom_uint8_t{test_vec.at(i)};
});
// copy the vector
vector_type<custom_uint8_t, size> left_vec{right_vec};
vector_type<custom_uint8_t, size> left_vec;
// check copy assignment op
left_vec = right_vec;
// overwrite right_vec with 0s
right_vec = vector_type<custom_uint8_t, size>{};
// check if values were copied correctly
ck::static_for<0, size, 1>{}([&](auto i) {
ASSERT_EQ(left_vec.template AsType<custom_uint8_t>()(Number<i>{}).data, test_vec.at(i));
......@@ -287,8 +296,11 @@ TEST(Custom_f8, TestAsType)
ck::static_for<0, size, 1>{}([&](auto i) {
right_vec.template AsType<custom_f8_t>()(Number<i>{}) = custom_f8_t{test_vec.at(i)};
});
// copy the vector
vector_type<custom_f8_t, size> left_vec{right_vec};
vector_type<custom_f8_t, size> left_vec;
// check copy assignment op
left_vec = right_vec;
// overwrite right_vec with 0s
right_vec = vector_type<custom_f8_t, size>{};
// check if values were copied correctly
ck::static_for<0, size, 1>{}([&](auto i) {
ASSERT_EQ(left_vec.template AsType<custom_f8_t>()(Number<i>{}).data, test_vec.at(i));
......@@ -369,8 +381,11 @@ TEST(Custom_bf8, TestAsType)
ck::static_for<0, size, 1>{}([&](auto i) {
right_vec.template AsType<custom_bf8_t>()(Number<i>{}) = custom_bf8_t{test_vec.at(i)};
});
// copy the vector
vector_type<custom_bf8_t, size> left_vec{right_vec};
vector_type<custom_bf8_t, size> left_vec;
// check copy assignment op
left_vec = right_vec;
// overwrite right_vec with 0s
right_vec = vector_type<custom_bf8_t, size>{};
// check if values were copied correctly
ck::static_for<0, size, 1>{}([&](auto i) {
ASSERT_EQ(left_vec.template AsType<custom_bf8_t>()(Number<i>{}).data, test_vec.at(i));
......@@ -450,8 +465,11 @@ TEST(Custom_half, TestAsType)
ck::static_for<0, size, 1>{}([&](auto i) {
right_vec.template AsType<custom_half_t>()(Number<i>{}) = custom_half_t{test_vec.at(i)};
});
// copy the vector
vector_type<custom_half_t, size> left_vec{right_vec};
vector_type<custom_half_t, size> left_vec;
// check copy assignment op
left_vec = right_vec;
// overwrite right_vec with 0s
right_vec = vector_type<custom_half_t, size>{};
// check if values were copied correctly
ck::static_for<0, size, 1>{}([&](auto i) {
ASSERT_EQ(left_vec.template AsType<custom_half_t>()(Number<i>{}).data, test_vec.at(i));
......@@ -533,8 +551,11 @@ TEST(Custom_bhalf, TestAsType)
ck::static_for<0, size, 1>{}([&](auto i) {
right_vec.template AsType<custom_bhalf_t>()(Number<i>{}) = custom_bhalf_t{test_vec.at(i)};
});
// copy the vector
vector_type<custom_bhalf_t, size> left_vec{right_vec};
vector_type<custom_bhalf_t, size> left_vec;
// check copy assignment op
left_vec = right_vec;
// overwrite right_vec with 0s
right_vec = vector_type<custom_bhalf_t, size>{};
// check if values were copied correctly
ck::static_for<0, size, 1>{}([&](auto i) {
ASSERT_EQ(left_vec.template AsType<custom_bhalf_t>()(Number<i>{}).data, test_vec.at(i));
......@@ -615,8 +636,11 @@ TEST(Custom_float, TestAsType)
ck::static_for<0, size, 1>{}([&](auto i) {
right_vec.template AsType<custom_float_t>()(Number<i>{}) = custom_float_t{test_vec.at(i)};
});
// copy the vector
vector_type<custom_float_t, size> left_vec{right_vec};
vector_type<custom_float_t, size> left_vec;
// check copy assignment op
left_vec = right_vec;
// overwrite right_vec with 0s
right_vec = vector_type<custom_float_t, size>{};
// check if values were copied correctly
ck::static_for<0, size, 1>{}([&](auto i) {
ASSERT_EQ(left_vec.template AsType<custom_float_t>()(Number<i>{}).data, test_vec.at(i));
......@@ -693,8 +717,11 @@ TEST(Custom_double, TestAsType)
ck::static_for<0, size, 1>{}([&](auto i) {
right_vec.template AsType<custom_double_t>()(Number<i>{}) = custom_double_t{test_vec.at(i)};
});
// copy the vector
vector_type<custom_double_t, size> left_vec{right_vec};
vector_type<custom_double_t, size> left_vec;
// check copy assignment op
left_vec = right_vec;
// overwrite right_vec with 0s
right_vec = vector_type<custom_double_t, size>{};
// check if values were copied correctly
ck::static_for<0, size, 1>{}([&](auto i) {
ASSERT_EQ(left_vec.template AsType<custom_double_t>()(Number<i>{}).data, test_vec.at(i));
......@@ -813,8 +840,11 @@ TEST(Complex_half, TestAsType)
right_vec.template AsType<complex_half_t>()(Number<i>{}) =
complex_half_t{test_vec.at(num_elem * i), test_vec.at(num_elem * i + 1)};
});
// copy the vector
vector_type<complex_half_t, size> left_vec{right_vec};
vector_type<complex_half_t, size> left_vec;
// check copy assignment op
left_vec = right_vec;
// overwrite right_vec with 0s
right_vec = vector_type<complex_half_t, size>{};
// check if values were copied correctly
ck::static_for<0, size, 1>{}([&](auto i) {
ASSERT_EQ(left_vec.template AsType<complex_half_t>()(Number<i>{}).real,
......@@ -907,8 +937,11 @@ TEST(FP8OCP, TestAsType)
right_vec.template AsType<f8_t>()(Number<i>{}) = ck::type_convert<f8_t>(test_vec.at(i));
});
// copy the vector
vector_type<f8_t, size> left_vec{right_vec};
vector_type<f8_t, size> left_vec;
// check copy assignment op
left_vec = right_vec;
// overwrite right_vec with 0s
right_vec = vector_type<f8_t, size>{};
// check if values were copied correctly
ck::static_for<0, size, 1>{}([&](auto i) {
......@@ -984,8 +1017,11 @@ TEST(BF8OCP, TestAsType)
right_vec.template AsType<bf8_t>()(Number<i>{}) = ck::type_convert<bf8_t>(test_vec.at(i));
});
// copy the vector
vector_type<bf8_t, size> left_vec{right_vec};
// check copy assignment op
left_vec = right_vec;
// overwrite right_vec with 0s
right_vec = vector_type<bf8_t, size>{};
// check if values were copied correctly
ck::static_for<0, size, 1>{}([&](auto i) {
......
test/grouped_convnd_bwd_data/CMakeLists.txt
View file @ 8b49f207
add_gtest_executable(test_grouped_convnd_bwd_data test_grouped_convnd_bwd_data_xdl_wmma.cpp)
add_gtest_executable(test_grouped_convnd_bwd_data_xdl test_grouped_convnd_bwd_data_xdl.cpp)
if(result EQUAL 0)
target_link_libraries(test_grouped_convnd_bwd_data PRIVATE utility device_grouped_conv2d_bwd_data_instance device_grouped_conv3d_bwd_data_instance)
target_link_libraries(test_grouped_convnd_bwd_data_xdl PRIVATE utility device_grouped_conv2d_bwd_data_instance device_grouped_conv3d_bwd_data_instance)
endif()
add_gtest_executable(test_grouped_convnd_bwd_data_wmma test_grouped_convnd_bwd_data_wmma.cpp)
if(result EQUAL 0)
target_link_libraries(test_grouped_convnd_bwd_data_wmma PRIVATE utility device_grouped_conv2d_bwd_data_instance device_grouped_conv3d_bwd_data_instance)
endif()
add_gtest_executable(test_grouped_convnd_bwd_data_interface_xdl test_grouped_convnd_bwd_data_interface_xdl.cpp)
if(result EQUAL 0)
......
test/grouped_convnd_bwd_data/test_grouped_convnd_bwd_data_wmma.cpp 0 → 100644
View file @ 8b49f207
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <iostream>
#include <initializer_list>
#include <tuple>
#include <vector>
#include <gtest/gtest.h>
#include "profiler/profile_grouped_conv_bwd_data_impl.hpp"
template <typename Tuple>
class TestGroupedConvndBwdDataWmma : public ::testing::Test
{
protected:
using DataType = std::tuple_element_t<0, Tuple>;
using OutLayout = std::tuple_element_t<1, Tuple>;
using WeiLayout = std::tuple_element_t<2, Tuple>;
using InLayout = std::tuple_element_t<3, Tuple>;
std::vector<ck::utils::conv::ConvParam> conv_params;
template <ck::index_t NDimSpatial>
void Run()
{
EXPECT_FALSE(conv_params.empty());
bool pass = true;
for(auto& param : conv_params)
{
pass = pass && ck::profiler::profile_grouped_conv_bwd_data_impl<NDimSpatial,
OutLayout,
WeiLayout,
InLayout,
DataType,
DataType,
DataType>(
true, // do_verification
1, // init_method: integer value
false, // do_log
false, // time_kernel
param);
}
EXPECT_TRUE(pass);
}
};
using namespace ck::tensor_layout::convolution;
using KernelTypes2d = ::testing::Types<std::tuple<ck::half_t, GNHWK, GKYXC, GNHWC>,
std::tuple<int8_t, GNHWK, GKYXC, GNHWC>,
std::tuple<ck::half_t, NHWGK, GKYXC, NHWGC>,
std::tuple<int8_t, NHWGK, GKYXC, NHWGC>>;
using KernelTypes3d = ::testing::Types<std::tuple<ck::half_t, GNDHWK, GKZYXC, GNDHWC>,
std::tuple<int8_t, GNDHWK, GKZYXC, GNDHWC>,
std::tuple<ck::half_t, NDHWGK, GKZYXC, NDHWGC>,
std::tuple<int8_t, NDHWGK, GKZYXC, NDHWGC>>;
template <typename Tuple>
class TestGroupedConvndBwdDataWmma2d : public TestGroupedConvndBwdDataWmma<Tuple>
{
};
template <typename Tuple>
class TestGroupedConvndBwdDataWmma3d : public TestGroupedConvndBwdDataWmma<Tuple>
{
};
TYPED_TEST_SUITE(TestGroupedConvndBwdDataWmma2d, KernelTypes2d);
TYPED_TEST_SUITE(TestGroupedConvndBwdDataWmma3d, KernelTypes3d);
TYPED_TEST(TestGroupedConvndBwdDataWmma2d, Test2D)
{
this->conv_params.clear();
this->conv_params.push_back(
{2, 2, 4, 192, 192, {3, 3}, {28, 28}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
this->conv_params.push_back(
{2, 2, 128, 128, 256, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
this->conv_params.push_back(
{2, 2, 128, 128, 256, {1, 1}, {7, 7}, {2, 2}, {1, 1}, {0, 0}, {0, 0}});
this->conv_params.push_back(
{2, 2, 128, 128, 256, {1, 1}, {3, 3}, {1, 1}, {1, 1}, {0, 0}, {0, 0}});
this->conv_params.push_back({2, 1, 1, 1, 32, {8, 8}, {32, 32}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
this->conv_params.push_back({2, 1, 1, 64, 3, {8, 8}, {32, 32}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
this->conv_params.push_back({2, 1, 1, 1, 1, {8, 8}, {32, 32}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
this->template Run<2>();
}
TYPED_TEST(TestGroupedConvndBwdDataWmma3d, Test3D)
{
this->conv_params.clear();
this->conv_params.push_back(
{3, 2, 16, 128, 256, {1, 1, 1}, {7, 7, 7}, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
this->conv_params.push_back(
{3, 2, 2, 128, 256, {3, 3, 3}, {14, 14, 3}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
this->conv_params.push_back(
{3, 2, 32, 128, 256, {1, 1, 1}, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
this->conv_params.push_back(
{3, 1, 1, 1, 32, {3, 3, 3}, {32, 32, 32}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
this->conv_params.push_back(
{3, 1, 1, 64, 3, {3, 3, 3}, {32, 32, 32}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
this->conv_params.push_back(
{3, 1, 1, 1, 1, {3, 3, 3}, {32, 32, 32}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
this->template Run<3>();
}
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